Download - Biomarker Analyst_Presentation_For_Client
© 2015 Cognizant © 2015 Cognizant
June 8, 2015
Biomarker and Target Analyst: A new platform using data science to improve Pharma R&D outcomesCamille Diges, PhD
© 2015 Cognizant
Personalized medicine is driving rapid biomarker market growth
Data from Frost & Sullivan, 2013;Markets and Markets, 2014
IT R&D AND DISCOVERY R&D TEAMS MUST ADAPT TO NEW
MARKET NEEDS
IT R&D CHALLENGES• ‘OMICS TECHNOLOGIES CREATING MORE DATA
AT FASTER RATES EVERY YEAR
• NEW INFRASTRUCTURE REQUIRED TO HANDLE DATA ANALYSIS QUICKLY
• KEEPING ALL INTERNAL AND EXTERNAL DATA SOURCES UP TO DATE
DISCOVERY R&D CHALLENGES• DATA ANALYSIS REQUIRES SPECIALIZED
PROGRAMMING ABILITIES
• DIFFICULT TO PLACE RESULTS IN BIOLOGICAL CONTEXT
• DIFFICULT TO INTEGRATE AND VISUALIZE DATA GENERATED FROM DIFFERENT EXPERIMENTS
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Biomarker Analyst: helping make personalized medicine a reality
METABOLITES
BIOMARKERANALYST
PLATFORMPROTEINCHANGES
EPIGENETICS
GENEEXPRESSION
GENOMESEQUENCE
miRNAEXPRESSION
• IMPROVED DATA ANALYSIS
• INCREASED DATA ACCESSIBILITY
• DATA INTEGRATION ACROSS EXPERIMENTS
• FUNCTIONAL FRAMEWORK FOR RESULTS
PLATFORM CAPABILITIES
BUSINESS VALUE• FASTER TIME TO MARKET FOR NEW DRUGS
• REDUCED FAILURE RATE IN CLINICAL TRIALS
• BIOMARKER AND TARGET IDENTIFICATION
• BETTER UNDERSTANDING OF DISEASE
• NEW RESEARCH DISCOVERIES
© 2015 Cognizant
Biomarker Analyst: Combining experimental analysis with functional information
Data Sources
Gene expression
DNA Sequencing
Epigenetic Data
Pathway Databases
Biomarker Analyst
Proteomic Data
© 2015 Cognizant
General Biomarker Analyst infrastructure overview
Storage Layer
Data Sources
StructuredData
UnstructuredData
Publications
NCBI
Reactome
PathwayDatabasesGenome
SequencesMass
Spectra
ChIPData
ExternalData
Local FileSystem
Big Data Storage & Processing Consumption LayerAnalytics &
Visualization
R&D Scientists
Biomarker Analyst democratizes data analysis, provides access to the most up-to-date analysis packages and scientific results, and enables the integration of data from different types of experiments to provide a holistic and functional view of the disease state.
© 2015 Cognizant
Proof of Concept: Identifying new biomarkers for breast cancer relapse
© 2015 Cognizant 7
Application: Linking gene expression to phenotype to identify biomarkersClient Challenge: • “Biomarkers” routinely identified, but fail at various stages of drug
development. Costly failures ($MM).• Gene expression data is time consuming and statistically challenging
to analyze. Misinterpretation is common. No functional information.• Biomarker success dependent on understanding its function.
Cognizant Solution: • Cognizant coupled gene expression analysis to signaling pathway
data to identify functional biomarkers for breast cancer relapse• Differential gene expression algorithms implemented to add rigor to
data analysis• Hadoop and R used to decrease processing time from days to hours
Client Benefit: • Differential gene expression analysis reduced candidate biomarkers
from 2,300+ genes to 10 gene pairs• 7 potential biomarkers identified for breast cancer relapse when placed
in functional context
IndustryPharmaceutical
EnvironmentHadoopR, RstudioNCBI
ChallengeData normalizationStatistical analysisIdentification of important gene pairs
Cognizant SolutionAutomate process to shorten analysis time from days to minutes
Client BenefitBM IdentificationFunctional analysis of BMCompanion testsCompanion diagnostics
© 2015 Cognizant
Biomarker Analyst places results in a functional context
Microarray Data from
GEO
Pathway Data
Data Collection
Generating Possible Pairs on Pathway
List of Differential Gene Paris
List of Gene Pairs on inter-
connected Pathways
Joint Lists of Gene Pairs Map Pairs on Pathway MapBio-Marker Network Charts
Grouping Normalization
Name Matching
ANOVA Test Filter
Differential Genes
Correlation for all pairs of differential
genes
Keep strongly related gene
pairs only
• Use the Differential Genes to form pairs• Examine pairs in the functional context of Reactome Pathways
Phase1
Phase2
Phase 3
Note: • Gene Selection, Pearson’s Correlation in Phase 1 AND Generating Pairs on Pathway in Phase 2 improved by applying Hadoop• If desired, Gene Selection Step may be taken out from process to keep more genes after Hadoop applied.
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Down-regulated gene pairs correlated to high relapse rates of breast cancer
“Kinome Expression Profiling and Prognosis of Basal Breast Cancers”, Sabatier et al, Molecular Cancer, 2011
• Our analysis identified 7 key proteins that are significantly down-regulated in relapsed breast cancer patients
• All seven genes are part of the “Immune Metagene”
• 51% of patients with down-regulation of the Immune Metagene will have breast cancer relapse in 5 years compared to only 9% with no changes in gene expression.
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Biomarker Analyst Platform: Additional Features
• Multiple data sources for analysis to reflect complexity of experimental landscape
• DNA sequencing, metabolomic and proteomic data, CHiP-Seq and RNA-Seq
• Capable of working with any signaling pathway databases• Ingenuity IPA, GeneGo, Pathway Studio, KEGG, etc.
• Integrate results from different experiment types• Not possible currently due to data handling, analytical, and statistical challenges
• Customize visualization of results• Designed to meet scientific needs
• Phase 2 will automatically connect results to key publications using semantic technology
• Provide instant scientific context for results
© 2015 Cognizant
Camille [email protected]